Head to head · Decision models · October 2026 research run

Drex Max v1 vs joey-1

joey-1 scores 57.1 (C) on agent readiness against Drex Max v1's 55.5 (C), and leads in 2 of 4 scored categories. Drex Max v1 leads on reach and agent interface. Both do decision models.

Best decision models for AI agents · All 200 decisions comparisons

Which one, for what

Drex Max v1 C

Best for Agents that want self-hosted routing, triage or screen decisions with probabilities, on a CUDA GPU with 48 GB or more for images or a 24 GB Mac for text, where the Open RAIL-M terms fit.

Ahead on

  • Reach, 65 against 60
  • Agent interface, 80 against 70

Watch for

The licence bars commercial use above $1 million in revenue or funding and by anyone with a competing product, and restricts the outputs too

joey-1 C

Best for Agents that want self-hosted, low-cost ticket routing or guardrail checks with probabilities to threshold, on a GPU with 8 GB, and can accept a patched runtime.

Ahead on

  • Risk, 55 against 45
  • Documentation and upkeep, 54 against 41

Watch for

Serving it needs OneJev at a pinned commit plus the repository's serve.patch, with no released runtime support

Score by category

CategoryWeight this runDrex Max v1joey-1Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256560Drex Max v1 +5
Risk20%204555joey-1 +10
Agent interface10%108070Drex Max v1 +10
Documentation and upkeep10%104154joey-1 +13
Negative events≤15-2-2
TotalC 55.5/100C 57.1/100

Facts side by side

FactDrex Max v1joey-1
KindOpen-weight modelOpen-weight model
VendorNace AI, Inc. (Nace.AI)Baseten
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceNace.AI Open RAIL-M License (modified), version 1.0, October 2026, in the repository's LICENSE. Not an open-source licence. Use is free only for individuals and organisations under $1 million in revenue in the prior 12 months and under $1 million in total funding (personal and research use excepted), never by anyone with a product that competes with Nace's, decision models included, and under use restrictions that bind the model, its derivatives and its outputs. Built on Qwen/Qwen3.8-27B (Apache-2.0), whose licence text isn't carried in the repositoryMIT, named in the card's metadata (license: mit) since the upload of 27 October 2026, with no LICENSE file. The base Qwen/Qwen3.5-2B is Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-10-302026-10-29
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver

Verdicts

Drex Max v1

A 27B cousin for typed decisions over text and screenshots, several questions per request with a probability for each option. Images need an NVIDIA GPU and Nace's loader; on Apple silicon only the text backbone runs, from 24 GB. The licence isn't open source, with free commercial use only under $1 million in revenue and funding.

joey-1

A very small cousin for routing, triage and guardrail checks, answering several typed questions about one state, with text or images, as probabilities from one prefill. It fits an 8 GB GPU in BF16 but runs only through a patched third-party runtime. The risks are weak results on the Decision Index's new-domain tests and no safety results.

Before you call either

Drex Max v1

  1. Clone github.com/nace-ai/drex-decision-models, download nace-ai/drex-max-v1 and run python -m drex_max --model weights/drex-max-v1 --request examples/request.json on an NVIDIA GPU
  2. Plan for about 49 GB of GPU memory in BF16, or about 26 GB with --8bit on CUDA, before any long context
  3. Don't load it with stock Transformers, vLLM or llama.cpp. Its full-attention layers must run non-causally with readout.safetensors applied, which only Nace's loader and forks do
  4. On a Mac with 24 GB or more, run kapitsa-systemone --model ./drex-max-v1-affine-q4 --port 8000 --context 2048 for text-only decisions at POST /v1/systemone
  5. Raise the 16,384-token default with DREX_MAX_CONTEXT=N or --max-length N, up to 262,144, and expect more memory
  6. Check the licence first. Above $1 million in revenue or funding, or with a competing product, you need Nace's commercial licence

joey-1

  1. Start on a CUDA GPU in pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel with hf download baselabs/joey-1 --local-dir joey-1 && bash joey-1/setup.sh, which clones OneJev at 81ce62f, applies serve.patch and installs the Decision Index kit
  2. Then call DecisionEngine("baselabs/joey-1", device="cuda:0").decide(SystemOneRequest(state=..., questions=...)) from qev, as the card shows
  3. Don't load it with stock vLLM or Transformers. They ignore the block-bidirectional attention in config.json's modeling_overrides and give probabilities the model wasn't trained for
  4. Keep each request under 16,384 tokens, the trained length, or the engine refuses it
  5. Expect weaker answers on domains unlike its training mix, and threshold on the returned probability before acting
  6. Without hardware, Baseten's /v1/systemone serves it as decision-models/baseten-joey-2b-latest with a Baseten key

Questions

Which is better for AI agents, Drex Max v1 or joey-1?

joey-1 scores 57.1 (C) on agent readiness against Drex Max v1's 55.5 (C), and leads in 2 of 4 scored categories. Drex Max v1 leads on reach and agent interface.

Other comparisons with Drex Max v1 or joey-1

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